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6,800 | Scalable Kernel K-Means Clustering with Nystrom Approximation:
Relative-Error Bounds | cs.LG | Kernel $k$-means clustering can correctly identify and extract a far more
varied collection of cluster structures than the linear $k$-means clustering
algorithm. However, kernel $k$-means clustering is computationally expensive
when the non-linear feature map is high-dimensional and there are many input
points.
Kerne... | computer science |
6,801 | Assessing the Performance of Deep Learning Algorithms for Newsvendor
Problem | stat.ML | In retailer management, the Newsvendor problem has widely attracted attention
as one of basic inventory models. In the traditional approach to solving this
problem, it relies on the probability distribution of the demand. In theory, if
the probability distribution is known, the problem can be considered as fully
solved... | computer science |
6,802 | Monte-Carlo Tree Search by Best Arm Identification | stat.ML | Recent advances in bandit tools and techniques for sequential learning are
steadily enabling new applications and are promising the resolution of a range
of challenging related problems. We study the game tree search problem, where
the goal is to quickly identify the optimal move in a given game tree by
sequentially sa... | computer science |
6,803 | Group Invariance, Stability to Deformations, and Complexity of Deep
Convolutional Representations | stat.ML | In this paper, we study deep signal representations that are invariant to
groups of transformations and stable to the action of diffeomorphisms without
losing signal information. This is achieved by generalizing the multilayer
kernel construction introduced in the context of convolutional kernel networks
and by studyin... | computer science |
6,804 | An Expectation-Maximization Algorithm for the Fractal Inverse Problem | stat.ML | We present an Expectation-Maximization algorithm for the fractal inverse
problem: the problem of fitting a fractal model to data. In our setting the
fractals are Iterated Function Systems (IFS), with similitudes as the family of
transformations. The data is a point cloud in ${\mathbb R}^H$ with arbitrary
dimension $H$.... | computer science |
6,805 | Stepwise regression for unsupervised learning | cs.LG | I consider unsupervised extensions of the fast stepwise linear regression
algorithm \cite{efroymson1960multiple}. These extensions allow one to
efficiently identify highly-representative feature variable subsets within a
given set of jointly distributed variables. This in turn allows for the
efficient dimensional reduc... | computer science |
6,806 | An Online Learning Approach to Generative Adversarial Networks | cs.LG | We consider the problem of training generative models with a Generative
Adversarial Network (GAN). Although GANs can accurately model complex
distributions, they are known to be difficult to train due to instabilities
caused by a difficult minimax optimization problem. In this paper, we view the
problem of training GAN... | computer science |
6,807 | Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for
Electronic Health Record (EHR) Analysis | cs.LG | The past decade has seen an explosion in the amount of digital information
stored in electronic health records (EHR). While primarily designed for
archiving patient clinical information and administrative healthcare tasks,
many researchers have found secondary use of these records for various clinical
informatics tasks... | computer science |
6,808 | Confident Multiple Choice Learning | cs.LG | Ensemble methods are arguably the most trustworthy techniques for boosting
the performance of machine learning models. Popular independent ensembles (IE)
relying on naive averaging/voting scheme have been of typical choice for most
applications involving deep neural networks, but they do not consider advanced
collabora... | computer science |
6,809 | Random Forests, Decision Trees, and Categorical Predictors: The "Absent
Levels" Problem | stat.ML | One of the advantages that decision trees have over many other models is
their ability to natively handle categorical predictors without having to first
transform them (e.g., by using one-hot encoding). However, in this paper, we
show how this capability can also lead to an inherent "absent levels" problem
for decision... | computer science |
6,810 | Recursive Multikernel Filters Exploiting Nonlinear Temporal Structure | stat.ML | In kernel methods, temporal information on the data is commonly included by
using time-delayed embeddings as inputs. Recently, an alternative formulation
was proposed by defining a gamma-filter explicitly in a reproducing kernel
Hilbert space, giving rise to a complex model where multiple kernels operate on
different t... | computer science |
6,811 | SEVEN: Deep Semi-supervised Verification Networks | cs.LG | Verification determines whether two samples belong to the same class or not,
and has important applications such as face and fingerprint verification, where
thousands or millions of categories are present but each category has scarce
labeled examples, presenting two major challenges for existing deep learning
models. W... | computer science |
6,812 | Convergence analysis of belief propagation for pairwise linear Gaussian
models | cs.LG | Gaussian belief propagation (BP) has been widely used for distributed
inference in large-scale networks such as the smart grid, sensor networks, and
social networks, where local measurements/observations are scattered over a
wide geographical area. One particular case is when two neighboring agents
share a common obser... | computer science |
6,813 | Lost Relatives of the Gumbel Trick | stat.ML | The Gumbel trick is a method to sample from a discrete probability
distribution, or to estimate its normalizing partition function. The method
relies on repeatedly applying a random perturbation to the distribution in a
particular way, each time solving for the most likely configuration. We derive
an entire family of r... | computer science |
6,814 | On Optimistic versus Randomized Exploration in Reinforcement Learning | stat.ML | We discuss the relative merits of optimistic and randomized approaches to
exploration in reinforcement learning. Optimistic approaches presented in the
literature apply an optimistic boost to the value estimate at each state-action
pair and select actions that are greedy with respect to the resulting
optimistic value f... | computer science |
6,815 | SEARNN: Training RNNs with Global-Local Losses | cs.LG | We propose SEARNN, a novel training algorithm for recurrent neural networks
(RNNs) inspired by the "learning to search" (L2S) approach to structured
prediction. RNNs have been widely successful in structured prediction
applications such as machine translation or parsing, and are commonly trained
using maximum likelihoo... | computer science |
6,816 | Provable benefits of representation learning | cs.LG | There is general consensus that learning representations is useful for a
variety of reasons, e.g. efficient use of labeled data (semi-supervised
learning), transfer learning and understanding hidden structure of data.
Popular techniques for representation learning include clustering, manifold
learning, kernel-learning,... | computer science |
6,817 | A Practical Method for Solving Contextual Bandit Problems Using Decision
Trees | cs.LG | Many efficient algorithms with strong theoretical guarantees have been
proposed for the contextual multi-armed bandit problem. However, applying these
algorithms in practice can be difficult because they require domain expertise
to build appropriate features and to tune their parameters. We propose a new
method for the... | computer science |
6,818 | Reinforcement Learning under Model Mismatch | cs.LG | We study reinforcement learning under model misspecification, where we do not
have access to the true environment but only to a reasonably close
approximation to it. We address this problem by extending the framework of
robust MDPs to the model-free Reinforcement Learning setting, where we do not
have access to the mod... | computer science |
6,819 | Stochastic Training of Neural Networks via Successive Convex
Approximations | stat.ML | This paper proposes a new family of algorithms for training neural networks
(NNs). These are based on recent developments in the field of non-convex
optimization, going under the general name of successive convex approximation
(SCA) techniques. The basic idea is to iteratively replace the original
(non-convex, highly d... | computer science |
6,820 | Second-Order Kernel Online Convex Optimization with Adaptive Sketching | stat.ML | Kernel online convex optimization (KOCO) is a framework combining the
expressiveness of non-parametric kernel models with the regret guarantees of
online learning. First-order KOCO methods such as functional gradient descent
require only $\mathcal{O}(t)$ time and space per iteration, and, when the only
information on t... | computer science |
6,821 | Robust Submodular Maximization: A Non-Uniform Partitioning Approach | stat.ML | We study the problem of maximizing a monotone submodular function subject to
a cardinality constraint $k$, with the added twist that a number of items
$\tau$ from the returned set may be removed. We focus on the worst-case setting
considered in (Orlin et al., 2016), in which a constant-factor approximation
guarantee wa... | computer science |
6,822 | FreezeOut: Accelerate Training by Progressively Freezing Layers | stat.ML | The early layers of a deep neural net have the fewest parameters, but take up
the most computation. In this extended abstract, we propose to only train the
hidden layers for a set portion of the training run, freezing them out
one-by-one and excluding them from the backward pass. Through experiments on
CIFAR, we empiri... | computer science |
6,823 | Variational Approaches for Auto-Encoding Generative Adversarial Networks | stat.ML | Auto-encoding generative adversarial networks (GANs) combine the standard GAN
algorithm, which discriminates between real and model-generated data, with a
reconstruction loss given by an auto-encoder. Such models aim to prevent mode
collapse in the learned generative model by ensuring that it is grounded in all
the ava... | computer science |
6,824 | One Model To Learn Them All | cs.LG | Deep learning yields great results across many fields, from speech
recognition, image classification, to translation. But for each problem,
getting a deep model to work well involves research into the architecture and a
long period of tuning. We present a single model that yields good results on a
number of problems sp... | computer science |
6,825 | Learning with Feature Evolvable Streams | cs.LG | Learning with streaming data has attracted much attention during the past few
years. Though most studies consider data stream with fixed features, in real
practice the features may be evolvable. For example, features of data gathered
by limited-lifespan sensors will change when these sensors are substituted by
new ones... | computer science |
6,826 | Unsupervised Domain Adaptation with Random Walks on Target Labelings | stat.ML | Unsupervised Domain Adaptation (DA) is used to automatize the task of
labeling data: an unlabeled dataset (target) is annotated using a labeled
dataset (source) from a related domain. We cast domain adaptation as the
problem of finding stable labels for target examples. A new definition of label
stability is proposed, ... | computer science |
6,827 | L2 Regularization versus Batch and Weight Normalization | cs.LG | Batch Normalization is a commonly used trick to improve the training of deep
neural networks. These neural networks use L2 regularization, also called
weight decay, ostensibly to prevent overfitting. However, we show that L2
regularization has no regularizing effect when combined with normalization.
Instead, regulariza... | computer science |
6,828 | Local Feature Descriptor Learning with Adaptive Siamese Network | cs.LG | Although the recent progress in the deep neural network has led to the
development of learnable local feature descriptors, there is no explicit answer
for estimation of the necessary size of a neural network. Specifically, the
local feature is represented in a low dimensional space, so the neural network
should have mo... | computer science |
6,829 | Expected Policy Gradients | stat.ML | We propose expected policy gradients (EPG), which unify stochastic policy
gradients (SPG) and deterministic policy gradients (DPG) for reinforcement
learning. Inspired by expected sarsa, EPG integrates across the action when
estimating the gradient, instead of relying only on the action in the sampled
trajectory. We es... | computer science |
6,830 | A Closer Look at Memorization in Deep Networks | stat.ML | We examine the role of memorization in deep learning, drawing connections to
capacity, generalization, and adversarial robustness. While deep networks are
capable of memorizing noise data, our results suggest that they tend to
prioritize learning simple patterns first. In our experiments, we expose
qualitative differen... | computer science |
6,831 | Rgtsvm: Support Vector Machines on a GPU in R | stat.ML | Rgtsvm provides a fast and flexible support vector machine (SVM)
implementation for the R language. The distinguishing feature of Rgtsvm is that
support vector classification and support vector regression tasks are
implemented on a graphical processing unit (GPU), allowing the libraries to
scale to millions of examples... | computer science |
6,832 | Sample, computation vs storage tradeoffs for classification using tensor
subspace models | cs.LG | In this paper, we exhibit the tradeoffs between the (training) sample,
computation and storage complexity for the problem of supervised classification
using signal subspace estimation. Our main tool is the use of tensor subspaces,
i.e. subspaces with a Kronecker structure, for embedding the data into lower
dimensions. ... | computer science |
6,833 | An a Priori Exponential Tail Bound for k-Folds Cross-Validation | stat.ML | We consider a priori generalization bounds developed in terms of
cross-validation estimates and the stability of learners. In particular, we
first derive an exponential Efron-Stein type tail inequality for the
concentration of a general function of n independent random variables. Next,
under some reasonable notion of s... | computer science |
6,834 | SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning
Dynamics and Interpretability | stat.ML | We propose a new technique, Singular Vector Canonical Correlation Analysis
(SVCCA), a tool for quickly comparing two representations in a way that is both
invariant to affine transform (allowing comparison between different layers and
networks) and fast to compute (allowing more comparisons to be calculated than
with p... | computer science |
6,835 | Deep Counterfactual Networks with Propensity-Dropout | cs.LG | We propose a novel approach for inferring the individualized causal effects
of a treatment (intervention) from observational data. Our approach
conceptualizes causal inference as a multitask learning problem; we model a
subject's potential outcomes using a deep multitask network with a set of
shared layers among the fa... | computer science |
6,836 | On comparing clusterings: an element-centric framework unifies overlaps
and hierarchy | stat.ML | Clustering is one of the most universal approaches for understanding complex
data. A pivotal aspect of clustering analysis is quantitatively comparing
clusterings; clustering comparison is the basis for tasks such as clustering
evaluation, consensus clustering, and tracking the temporal evolution of
clusters. For examp... | computer science |
6,837 | Unperturbed: spectral analysis beyond Davis-Kahan | stat.ML | Classical matrix perturbation results, such as Weyl's theorem for eigenvalues
and the Davis-Kahan theorem for eigenvectors, are general purpose. These
classical bounds are tight in the worst case, but in many settings sub-optimal
in the typical case. In this paper, we present perturbation bounds which
consider the natu... | computer science |
6,838 | A Divergence Bound for Hybrids of MCMC and Variational Inference and an
Application to Langevin Dynamics and SGVI | cs.LG | Two popular classes of methods for approximate inference are Markov chain
Monte Carlo (MCMC) and variational inference. MCMC tends to be accurate if run
for a long enough time, while variational inference tends to give better
approximations at shorter time horizons. However, the amount of time needed for
MCMC to exceed... | computer science |
6,839 | A giant with feet of clay: on the validity of the data that feed machine
learning in medicine | cs.LG | This paper considers the use of Machine Learning (ML) in medicine by focusing
on the main problem that this computational approach has been aimed at solving
or at least minimizing: uncertainty. To this aim, we point out how uncertainty
is so ingrained in medicine that it biases also the representation of clinical
pheno... | computer science |
6,840 | Analysis of dropout learning regarded as ensemble learning | cs.LG | Deep learning is the state-of-the-art in fields such as visual object
recognition and speech recognition. This learning uses a large number of
layers, huge number of units, and connections. Therefore, overfitting is a
serious problem. To avoid this problem, dropout learning is proposed. Dropout
learning neglects some i... | computer science |
6,841 | Concept Drift and Anomaly Detection in Graph Streams | cs.LG | Graph representations offer powerful and intuitive ways to describe data in a
multitude of application domains. Here, we consider stochastic processes
generating graphs and propose a methodology for detecting changes in
stationarity of such processes. The methodology is general and considers a
process generating attrib... | computer science |
6,842 | Statistical Mechanics of Node-perturbation Learning with Noisy Baseline | stat.ML | Node-perturbation learning is a type of statistical gradient descent
algorithm that can be applied to problems where the objective function is not
explicitly formulated, including reinforcement learning. It estimates the
gradient of an objective function by using the change in the object function in
response to the per... | computer science |
6,843 | Deep Interest Network for Click-Through Rate Prediction | stat.ML | To better extract users' interest by exploiting the rich historical behavior
data is crucial for building the click-through rate (CTR) prediction model in
the online advertising system in e-commerce industry. There are two key
observations on user behavior data: i) \textbf{diversity}. Users are interested
in different ... | computer science |
6,844 | The energy landscape of a simple neural network | stat.ML | We explore the energy landscape of a simple neural network. In particular, we
expand upon previous work demonstrating that the empirical complexity of fitted
neural networks is vastly less than a naive parameter count would suggest and
that this implicit regularization is actually beneficial for generalization
from fit... | computer science |
6,845 | A Note on Learning Algorithms for Quadratic Assignment with Graph Neural
Networks | stat.ML | Many inverse problems are formulated as optimization problems over certain
appropriate input distributions. Recently, there has been a growing interest in
understanding the computational hardness of these optimization problems, not
only in the worst case, but in an average-complexity sense under this same
input distrib... | computer science |
6,846 | Efficient Approximate Solutions to Mutual Information Based Global
Feature Selection | cs.LG | Mutual Information (MI) is often used for feature selection when developing
classifier models. Estimating the MI for a subset of features is often
intractable. We demonstrate, that under the assumptions of conditional
independence, MI between a subset of features can be expressed as the
Conditional Mutual Information (... | computer science |
6,847 | A Variance Maximization Criterion for Active Learning | stat.ML | Active learning aims to train a classifier as fast as possible with as few
labels as possible. The core element in virtually any active learning strategy
is the criterion that measures the usefulness of the unlabeled data based on
which new points to be labeled are picked. We propose a novel approach which we
refer to ... | computer science |
6,848 | Collaborative Deep Learning in Fixed Topology Networks | stat.ML | There is significant recent interest to parallelize deep learning algorithms
in order to handle the enormous growth in data and model sizes. While most
advances focus on model parallelization and engaging multiple computing agents
via using a central parameter server, aspect of data parallelization along with
decentral... | computer science |
6,849 | Methods for Interpreting and Understanding Deep Neural Networks | cs.LG | This paper provides an entry point to the problem of interpreting a deep
neural network model and explaining its predictions. It is based on a tutorial
given at ICASSP 2017. It introduces some recently proposed techniques of
interpretation, along with theory, tricks and recommendations, to make most
efficient use of th... | computer science |
6,850 | Target contrastive pessimistic risk for robust domain adaptation | stat.ML | In domain adaptation, classifiers with information from a source domain adapt
to generalize to a target domain. However, an adaptive classifier can perform
worse than a non-adaptive classifier due to invalid assumptions, increased
sensitivity to estimation errors or model misspecification. Our goal is to
develop a doma... | computer science |
6,851 | An Effective Way to Improve YouTube-8M Classification Accuracy in Google
Cloud Platform | stat.ML | Large-scale datasets have played a significant role in progress of neural
network and deep learning areas. YouTube-8M is such a benchmark dataset for
general multi-label video classification. It was created from over 7 million
YouTube videos (450,000 hours of video) and includes video labels from a
vocabulary of 4716 c... | computer science |
6,852 | Cognitive Subscore Trajectory Prediction in Alzheimer's Disease | stat.ML | Accurate diagnosis of Alzheimer's Disease (AD) entails clinical evaluation of
multiple cognition metrics and biomarkers. Metrics such as the Alzheimer's
Disease Assessment Scale - Cognitive test (ADAS-cog) comprise multiple
subscores that quantify different aspects of a patient's cognitive state such
as learning, memor... | computer science |
6,853 | GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash
Equilibrium | cs.LG | Generative Adversarial Networks (GANs) excel at creating realistic images
with complex models for which maximum likelihood is infeasible. However, the
convergence of GAN training has still not been proved. We propose a two
time-scale update rule (TTUR) for training GANs with stochastic gradient
descent on arbitrary GAN... | computer science |
6,854 | Learning Local Feature Aggregation Functions with Backpropagation | cs.LG | This paper introduces a family of local feature aggregation functions and a
novel method to estimate their parameters, such that they generate optimal
representations for classification (or any task that can be expressed as a cost
function minimization problem). To achieve that, we compose the local feature
aggregation... | computer science |
6,855 | Forecasting and Granger Modelling with Non-linear Dynamical Dependencies | cs.LG | Traditional linear methods for forecasting multivariate time series are not
able to satisfactorily model the non-linear dependencies that may exist in
non-Gaussian series. We build on the theory of learning vector-valued functions
in the reproducing kernel Hilbert space and develop a method for learning
prediction func... | computer science |
6,856 | Preserving Differential Privacy in Convolutional Deep Belief Networks | cs.LG | The remarkable development of deep learning in medicine and healthcare domain
presents obvious privacy issues, when deep neural networks are built on users'
personal and highly sensitive data, e.g., clinical records, user profiles,
biomedical images, etc. However, only a few scientific studies on preserving
privacy in ... | computer science |
6,857 | Reexamining Low Rank Matrix Factorization for Trace Norm Regularization | cs.LG | Trace norm regularization is a widely used approach for learning low rank
matrices. A standard optimization strategy is based on formulating the problem
as one of low rank matrix factorization which, however, leads to a non-convex
problem. In practice this approach works well, and it is often computationally
faster tha... | computer science |
6,858 | An Actor-Critic Contextual Bandit Algorithm for Personalized Mobile
Health Interventions | stat.ML | Increasing technological sophistication and widespread use of smartphones and
wearable devices provide opportunities for innovative and highly personalized
health interventions. A Just-In-Time Adaptive Intervention (JITAI) uses
real-time data collection and communication capabilities of modern mobile
devices to deliver... | computer science |
6,859 | autoBagging: Learning to Rank Bagging Workflows with Metalearning | stat.ML | Machine Learning (ML) has been successfully applied to a wide range of
domains and applications. One of the techniques behind most of these successful
applications is Ensemble Learning (EL), the field of ML that gave birth to
methods such as Random Forests or Boosting. The complexity of applying these
techniques togeth... | computer science |
6,860 | Recovery of Missing Samples Using Sparse Approximation via a Convex
Similarity Measure | stat.ML | In this paper, we study the missing sample recovery problem using methods
based on sparse approximation. In this regard, we investigate the algorithms
used for solving the inverse problem associated with the restoration of missed
samples of image signal. This problem is also known as inpainting in the
context of image ... | computer science |
6,861 | Deep learning bank distress from news and numerical financial data | stat.ML | In this paper we focus our attention on the exploitation of the information
contained in financial news to enhance the performance of a classifier of bank
distress. Such information should be analyzed and inserted into the predictive
model in the most efficient way and this task deals with all the issues related
to tex... | computer science |
6,862 | Feature uncertainty bounding schemes for large robust nonlinear SVM
classifiers | stat.ML | We consider the binary classification problem when data are large and subject
to unknown but bounded uncertainties. We address the problem by formulating the
nonlinear support vector machine training problem with robust optimization. To
do so, we analyze and propose two bounding schemes for uncertainties associated
to ... | computer science |
6,863 | Graph Convolution: A High-Order and Adaptive Approach | cs.LG | In this paper, we presented a novel convolutional neural network framework
for graph modeling, with the introduction of two new modules specially designed
for graph-structured data: the $k$-th order convolution operator and the
adaptive filtering module. Importantly, our framework of High-order and
Adaptive Graph Convo... | computer science |
6,864 | Neural Sequence Model Training via $α$-divergence Minimization | stat.ML | We propose a new neural sequence model training method in which the objective
function is defined by $\alpha$-divergence. We demonstrate that the objective
function generalizes the maximum-likelihood (ML)-based and reinforcement
learning (RL)-based objective functions as special cases (i.e., ML corresponds
to $\alpha \... | computer science |
6,865 | Optimization Methods for Supervised Machine Learning: From Linear Models
to Deep Learning | stat.ML | The goal of this tutorial is to introduce key models, algorithms, and open
questions related to the use of optimization methods for solving problems
arising in machine learning. It is written with an INFORMS audience in mind,
specifically those readers who are familiar with the basics of optimization
algorithms, but le... | computer science |
6,866 | On Fairness, Diversity and Randomness in Algorithmic Decision Making | stat.ML | Consider a binary decision making process where a single machine learning
classifier replaces a multitude of humans. We raise questions about the
resulting loss of diversity in the decision making process. We study the
potential benefits of using random classifier ensembles instead of a single
classifier in the context... | computer science |
6,867 | Noisy Networks for Exploration | cs.LG | We introduce NoisyNet, a deep reinforcement learning agent with parametric
noise added to its weights, and show that the induced stochasticity of the
agent's policy can be used to aid efficient exploration. The parameters of the
noise are learned with gradient descent along with the remaining network
weights. NoisyNet ... | computer science |
6,868 | From Parity to Preference-based Notions of Fairness in Classification | stat.ML | The adoption of automated, data-driven decision making in an ever expanding
range of applications has raised concerns about its potential unfairness
towards certain social groups. In this context, a number of recent studies have
focused on defining, detecting, and removing unfairness from data-driven
decision systems. ... | computer science |
6,869 | Penalizing Unfairness in Binary Classification | cs.LG | We present a new approach for mitigating unfairness in learned classifiers.
In particular, we focus on binary classification tasks over individuals from
two populations, where, as our criterion for fairness, we wish to achieve
similar false positive rates in both populations, and similar false negative
rates in both po... | computer science |
6,870 | On Scalable Inference with Stochastic Gradient Descent | stat.ML | In many applications involving large dataset or online updating, stochastic
gradient descent (SGD) provides a scalable way to compute parameter estimates
and has gained increasing popularity due to its numerical convenience and
memory efficiency. While the asymptotic properties of SGD-based estimators have
been establi... | computer science |
6,871 | Location Dependent Dirichlet Processes | stat.ML | Dirichlet processes (DP) are widely applied in Bayesian nonparametric
modeling. However, in their basic form they do not directly integrate
dependency information among data arising from space and time. In this paper,
we propose location dependent Dirichlet processes (LDDP) which incorporate
nonparametric Gaussian proc... | computer science |
6,872 | Variance Regularizing Adversarial Learning | stat.ML | We introduce a novel approach for training adversarial models by replacing
the discriminator score with a bi-modal Gaussian distribution over the
real/fake indicator variables. In order to do this, we train the Gaussian
classifier to match the target bi-modal distribution implicitly through
meta-adversarial training. W... | computer science |
6,873 | Dual Supervised Learning | cs.LG | Many supervised learning tasks are emerged in dual forms, e.g.,
English-to-French translation vs. French-to-English translation, speech
recognition vs. text to speech, and image classification vs. image generation.
Two dual tasks have intrinsic connections with each other due to the
probabilistic correlation between th... | computer science |
6,874 | Rank Determination for Low-Rank Data Completion | cs.LG | Recently, fundamental conditions on the sampling patterns have been obtained
for finite completability of low-rank matrices or tensors given the
corresponding ranks. In this paper, we consider the scenario where the rank is
not given and we aim to approximate the unknown rank based on the location of
sampled entries an... | computer science |
6,875 | Multiscale sequence modeling with a learned dictionary | stat.ML | We propose a generalization of neural network sequence models. Instead of
predicting one symbol at a time, our multi-scale model makes predictions over
multiple, potentially overlapping multi-symbol tokens. A variation of the
byte-pair encoding (BPE) compression algorithm is used to learn the dictionary
of tokens that ... | computer science |
6,876 | Learning to Avoid Errors in GANs by Manipulating Input Spaces | stat.ML | Despite recent advances, large scale visual artifacts are still a common
occurrence in images generated by GANs. Previous work has focused on improving
the generator's capability to accurately imitate the data distribution
$p_{data}$. In this paper, we instead explore methods that enable GANs to
actively avoid errors b... | computer science |
6,877 | A simple efficient density estimator that enables fast systematic search | cs.LG | This paper introduces a simple and efficient density estimator that enables
fast systematic search. To show its advantage over commonly used kernel density
estimator, we apply it to outlying aspects mining. Outlying aspects mining
discovers feature subsets (or subspaces) that describe how a query stand out
from a given... | computer science |
6,878 | Learning Deep Energy Models: Contrastive Divergence vs. Amortized MLE | stat.ML | We propose a number of new algorithms for learning deep energy models and
demonstrate their properties. We show that our SteinCD performs well in term of
test likelihood, while SteinGAN performs well in terms of generating realistic
looking images. Our results suggest promising directions for learning better
models by ... | computer science |
6,879 | Structured Black Box Variational Inference for Latent Time Series Models | stat.ML | Continuous latent time series models are prevalent in Bayesian modeling;
examples include the Kalman filter, dynamic collaborative filtering, or dynamic
topic models. These models often benefit from structured, non mean field
variational approximations that capture correlations between time steps. Black
box variational... | computer science |
6,880 | ProtoDash: Fast Interpretable Prototype Selection | stat.ML | In this paper we propose an efficient algorithm ProtoDash for selecting
prototypical examples from complex datasets. Our generalizes the learn to
criticize (L2C) work by Kim et al. (2016) to not only select prototypes for a
given sparsity level $m$ but also to associate non-negative (for
interpretability) weights with ... | computer science |
6,881 | Wasserstein Distance Guided Representation Learning for Domain
Adaptation | stat.ML | Domain adaptation aims at generalizing a high-performance learner on a target
domain via utilizing the knowledge distilled from a source domain which has a
different but related data distribution. One solution to domain adaptation is
to learn domain invariant feature representations while the learned
representations sh... | computer science |
6,882 | Labeled Memory Networks for Online Model Adaptation | cs.LG | Augmenting a neural network with memory that can grow without growing the
number of trained parameters is a recent powerful concept with many exciting
applications. We propose a design of memory augmented neural networks (MANNs)
called Labeled Memory Networks (LMNs) suited for tasks requiring online
adaptation in class... | computer science |
6,883 | Complex and Holographic Embeddings of Knowledge Graphs: A Comparison | cs.LG | Embeddings of knowledge graphs have received significant attention due to
their excellent performance for tasks like link prediction and entity
resolution. In this short paper, we are providing a comparison of two
state-of-the-art knowledge graph embeddings for which their equivalence has
recently been established, i.e... | computer science |
6,884 | Early stopping for kernel boosting algorithms: A general analysis with
localized complexities | stat.ML | Early stopping of iterative algorithms is a widely-used form of
regularization in statistics, commonly used in conjunction with boosting and
related gradient-type algorithms. Although consistency results have been
established in some settings, such estimators are less well-understood than
their analogues based on penal... | computer science |
6,885 | Indefinite Kernel Logistic Regression | cs.LG | Traditionally, kernel learning methods requires positive definitiveness on
the kernel, which is too strict and excludes many sophisticated similarities,
that are indefinite, in multimedia area. To utilize those indefinite kernels,
indefinite learning methods are of great interests. This paper aims at the
extension of t... | computer science |
6,886 | Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic
Forecasting | cs.LG | Spatiotemporal forecasting has various applications in neuroscience, climate
and transportation domain. Traffic forecasting is one canonical example of such
learning task. The task is challenging due to (1) complex spatial dependency on
road networks, (2) non-linear temporal dynamics with changing road conditions
and (... | computer science |
6,887 | High-Performance FPGA Implementation of Equivariant Adaptive Separation
via Independence Algorithm for Independent Component Analysis | cs.LG | Independent Component Analysis (ICA) is a dimensionality reduction technique
that can boost efficiency of machine learning models that deal with probability
density functions, e.g. Bayesian neural networks. Algorithms that implement
adaptive ICA converge slower than their nonadaptive counterparts, however, they
are cap... | computer science |
6,888 | Deep Character-Level Click-Through Rate Prediction for Sponsored Search | stat.ML | Predicting the click-through rate of an advertisement is a critical component
of online advertising platforms. In sponsored search, the click-through rate
estimates the probability that a displayed advertisement is clicked by a user
after she submits a query to the search engine. Commercial search engines
typically rel... | computer science |
6,889 | Bayesian Models of Data Streams with Hierarchical Power Priors | cs.LG | Making inferences from data streams is a pervasive problem in many modern
data analysis applications. But it requires to address the problem of
continuous model updating and adapt to changes or drifts in the underlying data
generating distribution. In this paper, we approach these problems from a
Bayesian perspective c... | computer science |
6,890 | Learning Mixture of Gaussians with Streaming Data | cs.LG | In this paper, we study the problem of learning a mixture of Gaussians with
streaming data: given a stream of $N$ points in $d$ dimensions generated by an
unknown mixture of $k$ spherical Gaussians, the goal is to estimate the model
parameters using a single pass over the data stream. We analyze a streaming
version of ... | computer science |
6,891 | Variational Inference via Transformations on Distributions | stat.ML | Variational inference methods often focus on the problem of efficient model
optimization, with little emphasis on the choice of the approximating
posterior. In this paper, we review and implement the various methods that
enable us to develop a rich family of approximating posteriors. We show that
one particular method ... | computer science |
6,892 | Nonlinear Sequential Accepts and Rejects for Identification of Top Arms
in Stochastic Bandits | stat.ML | We address the M-best-arm identification problem in multi-armed bandits. A
player has a limited budget to explore K arms (M<K), and once pulled, each arm
yields a reward drawn (independently) from a fixed, unknown distribution. The
goal is to find the top M arms in the sense of expected reward. We develop an
algorithm ... | computer science |
6,893 | Low Dose CT Image Reconstruction With Learned Sparsifying Transform | stat.ML | A major challenge in computed tomography (CT) is to reduce X-ray dose to a
low or even ultra-low level while maintaining the high quality of reconstructed
images. We propose a new method for CT reconstruction that combines penalized
weighted-least squares reconstruction (PWLS) with regularization based on a
sparsifying... | computer science |
6,894 | Least Square Variational Bayesian Autoencoder with Regularization | stat.ML | In recent years Variation Autoencoders have become one of the most popular
unsupervised learning of complicated distributions.Variational Autoencoder
(VAE) provides more efficient reconstructive performance over a traditional
autoencoder. Variational auto enocders make better approximaiton than MCMC. The
VAE defines a ... | computer science |
6,895 | Efficient mixture model for clustering of sparse high dimensional binary
data | cs.LG | In this paper we propose a mixture model, SparseMix, for clustering of sparse
high dimensional binary data, which connects model-based with centroid-based
clustering. Every group is described by a representative and a probability
distribution modeling dispersion from this representative. In contrast to
classical mixtur... | computer science |
6,896 | Accelerated Variance Reduced Stochastic ADMM | cs.LG | Recently, many variance reduced stochastic alternating direction method of
multipliers (ADMM) methods (e.g.\ SAG-ADMM, SDCA-ADMM and SVRG-ADMM) have made
exciting progress such as linear convergence rates for strongly convex
problems. However, the best known convergence rate for general convex problems
is O(1/T) as opp... | computer science |
6,897 | Fast Amortized Inference and Learning in Log-linear Models with Randomly
Perturbed Nearest Neighbor Search | cs.LG | Inference in log-linear models scales linearly in the size of output space in
the worst-case. This is often a bottleneck in natural language processing and
computer vision tasks when the output space is feasibly enumerable but very
large. We propose a method to perform inference in log-linear models with
sublinear amor... | computer science |
6,898 | DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural
Networks | stat.ML | In this paper we develop a novel computational sensing framework for sensing
and recovering structured signals. When trained on a set of representative
signals, our framework learns to take undersampled measurements and recover
signals from them using a deep convolutional neural network. In other words, it
learns a tra... | computer science |
6,899 | SCAN: Learning Abstract Hierarchical Compositional Visual Concepts | stat.ML | The natural world is infinitely diverse, yet this diversity arises from a
relatively small set of coherent properties and rules, such as the laws of
physics or chemistry. We conjecture that biological intelligent systems are
able to survive within their diverse environments by discovering the
regularities that arise fr... | computer science |
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